Keras训练一个基本体系化的分类模型流程案例

import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils        # 导入keras提供的numpy工具包
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD

# 载入数据
(x_train,y_train),(x_test,y_test) = mnist.load_data()
print("x_shape:",x_train.shape,"y_shape:",y_train.shape)

# 将数据转换由（60000,28,28）转换为（60000,784）,并进行归一化除以255
x_train = x_train.reshape(x_train.shape[0],-1)/255.0
x_test = x_test.reshape(x_test.shape[0], -1)/255.0
print(x_train.shape,x_test.shape)

# 将数据转换为one-hot编码的格式
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)

# 创建模型，输入是784个神经元，输出为10个神经元
model = Sequential(
    [Dense(units=10,input_dim=784,bias_initializer="one",activation="softmax")]
)

# 自定义优化器
sgd=SGD(lr=0.01)
# 编译模型,定义优化器，loss_function,训练过程中计算准确率
# model.compile(optimizer=sgd,loss="mse",metrics=["accuracy"],)   # 使用均方差作为损失函数
model.compile(optimizer=sgd,loss="categorical_crossentropy",metrics=["accuracy"],)   # 使用交叉熵作为损失函数

# 喂入数据集，并规定每次喂入32张图像,设定迭代周期
model.fit(x_train,y_train,batch_size=32,epochs=100)

# 评估模型
loss,accuracy = model.evaluate(x_test, y_test)
print("test losee:",loss,"accuracy:",accuracy)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37

示例二：

import pickle

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np

from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import SGD
from keras.regularizers import l2
import matplotlib.pyplot as plt


data_dict = pickle.load(open('./data.pickle', 'rb'))

data = data_dict['data']
labels = data_dict['labels']
for index_data,data_arr in enumerate(data):
    if len(data_arr) != 42:
        print('---删除掉异常元素---', index_data)
        del data[index_data]
        del labels[index_data]

data=np.vstack(data)

print('+++++++++++++++++++',data.shape)
labels = np.asarray(data_dict['labels'])

x_train, x_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, shuffle=True, stratify=labels)

print(x_train.shape)


# 将数据集标签改为one-hot编码的格式
y_train = np_utils.to_categorical(y_train, num_classes=9)
y_test = np_utils.to_categorical(y_test, num_classes=9)


# 创建网络模型，输入是784个神经元，输出为10类
model = Sequential([
    Dense(units=200,input_dim=42,bias_initializer="one",activation="relu",kernel_regularizer=l2(0.0003)),
    # Dropout(0.5),

    Dense(units=500,bias_initializer="one",activation="relu",kernel_regularizer=l2(0.0003)),
    Dense(units=100,bias_initializer="one",activation="relu",kernel_regularizer=l2(0.0003)),
    # Dropout(0.5),
    Dense(units=9,bias_initializer="one",activation="softmax",kernel_regularizer=l2(0.0003))
])

# 定义优化器
sgd = SGD(lr=0.01)
# 编译模型，定义优化器，loss_function,训练过程计算准确率
model.compile(optimizer=sgd,loss="categorical_crossentropy",metrics=["accuracy"])

# 训练数据集
history =model.fit(x_train,y_train,batch_size=32,epochs=200,validation_data=(x_test,y_test))
model.save("classmodel.h5")  # 会保存成HDF5的文件，pip install h5py
# 此种模型保存方式为保存模型的通用方式，既可以保存模型的结构，又可以保存模型的参数

# 评估模型
loss,accuracy = model.evaluate(x_test, y_test)
print("test loss:",loss,"accuracy:",accuracy)



history_dict = history.history
print(history_dict.keys())

import matplotlib.pyplot as plt

"""********************绘制训练损失与验证损失的训练结果********************"""

# 纵坐标，所需绘制的数据
history_dict = history.history
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']

# 横坐标，步长
epochs = range(1, len(loss_values) + 1)

# 绘制图像
plt.plot(epochs, loss_values, 'bo', label='Training loss')
plt.plot(epochs, val_loss_values, 'b', label='Validation loss')

# 标题
plt.title('Training and validation loss')

# 横、纵坐标标签
plt.xlabel('Epochs')
plt.ylabel('Loss')

# 自适应标签的位置
plt.legend()

# 显示图像
plt.show()


"""********************绘制训练精度与验证精度的训练结果********************"""
# 清除图像
plt.clf()

acc = history_dict['acc']
val_acc = history_dict['val_acc']

plt.plot(epochs, val_acc, 'bo', label='Training acc')
plt.plot(epochs, acc, 'b', label='Validation acc')

plt.title('Training and validation acc')

plt.xlabel('Epochs')
plt.ylabel('Acc')

plt.legend()

plt.show()



# 加载模型并检测
from keras.models import load_model
model = load_model('classmodel.h5')    # 需要安装keras==2.0.4版本
result=model.predict(x_test)
print(result)








1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133